Stanford University, Spring Quarter 2018

The schedule below is tentative and will be updated (frequently) as we progress through the quarter.

In the table below, VMLS refers to the EE103 textbook, Introduction to Applied Linear Algebra - Vectors, Matrices, and Least Squares.

Date | Slides | Additional reading |

4/3 | introduction, supervised learning | VMLS, chapters 12 and section 13.1. |

4/5 | linear regression, validation | VMLS, section 13.1. |

4/10 | validation | VMLS, section 13.2. |

4/12 | features | VMLS, section 13.3. |

4/17 | features | VMLS, section 13.3. |

4/19 | regularization and house prices example | VMLS, section 15.4. |

4/24 | non-quadratic losses | |

4/26 | non-quadratic regularizers | |

5/1 | optimization | |

5/3 | prox-gradient method | |

5/8 | boolean classification | VMLS, chapter 14. |

5/10 | multi-class classification | VMLS, section 14.3. |

5/15 | multi-class classification | VMLS, section 14.3. |

5/17 | neural networks | |

5/22 | neural networks | |

5/24 | unsupervised learning | |

5/29 | principal component analysis | |

5/31 | principal component analysis | |

6/5 | ||